Decisional Needs of African, Caribbean, and Black Patients Diagnosed with Brain-Heart Conditions
Bibliographic record
Abstract
Equity-deserving groups, including African, Caribbean, and Black (ACB) populations, face barriers to equitable brain-heart healthcare. These barriers contribute to unmet decisional needs and challenges in making informed health decisions. For my master's thesis, I aimed to investigate the decisional needs of ACB patients with brain-heart conditions and the unique challenges they encounter during decision-making using an explanatory sequential mixed-methods design. We included participants from equity-deserving groups who participated in an ongoing parent study. We administered surveys and conducted semi-structured interviews with adult patients from the Ottawa Hospital, the University of Ottawa Heart Institute, and community organizations. Our work was guided by the Ottawa Decision Support Framework, PROGRESS-Plus framework, and intersectionality theory. Survey results from 23 participants facing a variety of brain-heart health decisions in the past 12 months revealed that seven (30.4%) participants experienced clinically significant decisional conflict and six (30.0%) experienced clinically significant decision regret. The common challenges that participants experienced during decision-making included worrying about choosing the wrong option (n=10, 50%) and feeling that brain implications were never part of the conversation for their heart condition (n=9, 45%). The interviews further demonstrated complex barriers contributing to their unmet decisional needs, such as difficulties in accessing health information, strong emotions, challenges with patient-clinician communication, mistrust, and barriers to healthcare access. We integrated these findings using joint displays. The insights gained from this study can inform the development of equitable decision-support interventions to effectively address the decisional needs of ACB patients with brain-heart conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".